Today, digital infrastructure is growing rapidly and is at a point where we can gather disparate data from around the world to help us make better decisions, correlations, and predictions. This in turn allows us to create better and smarter automated systems through techniques, such as machine learning. One industry where this is applicable is agriculture, and this is what is being featured here.

With just a few simple sensors and a relatively small, connected network, we have created an automated hydroponics growing system. Our system connects 20 plants to a central machine-learning server, which, over time, monitors all of the plants’ growth in relation to several controllable, environmental factors. Once enough data is gathered, the machine learning system will anticipate the needs of each plant to promote as much growth as possible. Each unit is its own contained growing environment that will automatically sustain the growth of a plant.

Each unit has sensors to regulate the pH and nutrients of the water delivered to the plants, and over time, it will send growth data to a central machine-learning system where the needs of each plant is anticipated to promote as much growth as possible.

With a system like this, a variety of industries, not just agriculture, can benefit from the wealth of data being collected by connected networks. This is just the first step…

Maker spaces and new manufacturing methods have allowed both hobbyists and enterprises to build products not previously possible. These products are becoming more complex, they are often composed of multiple subsystems that rely on the performance of each other to enable the product to function as a whole. Take your smartphone for example, tapping the touchscreen sets off a domino effect from hardware, to electrical systems, then to embedded software (and back again in reverse if tactile feedback is provided). To create more reliable final products we need to deeply understand these relationships, which a seeing space allows us to do.